title: Identification of water pathogens by Raman microspectroscopy.
abstract: Legionella species can be found living in water mostly in a viable but nonculturable state or associated with protozoa and complex biofilm formations. Isolation and afterwards identification of these pathogens from environmental samples by using common identification procedures based on cultivation are extremely difficult and prolonged. The development of fast and sensitive method based on the cultivation free identification of bacteria is necessary. In this study Raman microspectroscopy combined with multiclass support vector machines have been used to discriminate between Legionella and other common aquatic bacteria, to distinguish among clinically relevant Legionella species and to classify unknown Raman spectra for a fast and reliable identification. Recorded Raman spectra of the twenty-two Legionella species as well as the Raman spectra of Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa were utilized to build the classification model. Afterwards, independent Raman spectra of eleven species were used to identify them on the basis of the classification model that was created. The present study shows that Raman microspectroscopy can be used as a rapid and reliable method to distinguish between Legionella species recognized as human pathogens and to identify samples which are unknown to the model based on multiclass support vector machines (MC-SVM).
authors.affiliation: Institut für Physikalische Chemie and Abbe Center of Photonics, Friedrich-Schiller-Universität Jena, D-07743 Jena, Germany.
authors.forename: Dragana
authors.initials: D
authors.lastname: Kusić
authors.forename: Bernd
authors.initials: B
authors.lastname: Kampe
authors.forename: Petra
authors.initials: P
authors.lastname: Rösch
authors.forename: Jürgen
authors.initials: J
authors.lastname: Popp
mesh_terms: D001419:Bacteria / Q000145:classification / Q000302:isolation & purification*; D013059:Spectrum Analysis, Raman / Q000379:methods*; D060388:Support Vector Machine; D014871:Water Microbiology
keywords: Identification; Legionella species; Raman spectroscopy; Water pathogens